---
title: "Riesgo de importación de casos de nCoV2019 desde Wuhan"
author: "Ciencia Abierta, Fuente: EpiRisk.net"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include = FALSE}
library(flexdashboard)
library(shiny)
library(jsonlite)
library(maptools)
library(ggplot2)
library(tidyr)
library(dplyr)
library(purrr)
library(leaflet)
library(plotly)
library(raster)

cities <- read.csv("data/epirisk_cities.csv")
countries <- read.csv("data/epirisk_countries.csv")

```

Column {data-width=650}
-----------------------------------------------------------------------
### Riesgo relativo de casos de importación por país

```{r}
data(wrld_simpl)

##Correcting labels
wrld_simpl$NAME <- as.character(wrld_simpl$NAME)
wrld_simpl$NAME[grepl("unei", wrld_simpl$NAME)] <- "Brunei"
wrld_simpl$NAME[grepl("Cote", wrld_simpl$NAME)] <- "Côte d'Ivoire"
wrld_simpl$NAME[grepl("Democratic Republic of the Congo", wrld_simpl$NAME)] <- "Congo, Dem. Rep."
wrld_simpl$NAME[grepl("Egypt", wrld_simpl$NAME)] <- "Egypt, Arab Rep."
wrld_simpl$NAME[grepl("Iran", wrld_simpl$NAME)] <- "Iran"
wrld_simpl$NAME[grepl("Korea, Republic of", wrld_simpl$NAME)] <- "Korea, Rep."
wrld_simpl$NAME[grepl("Korea, Democratic People's Republic of", wrld_simpl$NAME)] <- "Korea, Dem. Rep."
wrld_simpl$NAME[grepl("Korea, Democratic People's Republic of", wrld_simpl$NAME)] <- "Korea, Dem. Rep."
wrld_simpl$NAME[grepl("Lao", wrld_simpl$NAME)] <- "Lao PDR"
wrld_simpl$NAME[grepl("Russia", wrld_simpl$NAME)] <- "Russian Federation"
wrld_simpl$NAME[grepl("Tanzania", wrld_simpl$NAME)] <- "Tanzania"
wrld_simpl$NAME[wrld_simpl$NAME == "United States"] <- "United States of America"
wrld_simpl$NAME[grepl("Viet", wrld_simpl$NAME)] <- "Vietnam"

wrld_simpl$Riesgo <- countries$risk[match(wrld_simpl$NAME, countries$label)]
wrld_simpl$Poblacion <- countries$population[match(wrld_simpl$NAME, countries$label)]
wrld_simpl$Riesgo <- wrld_simpl$Riesgo*100


wrld_simpl$Poblacion[grepl("China", wrld_simpl$NAME)] <- 1381110000


# provide a custom tooltip to plotly with the county name and actual rate
polygon_popup2 <- paste0("País: ", wrld_simpl$NAME, "
",
                         "Población: ", wrld_simpl$Poblacion, "
",
                          "Riesgo: ", round(wrld_simpl$Riesgo,4), "%")
#create a color palette to fill the polygons
pal <- colorQuantile("Greens", domain = NULL, n = 10, na.color = "white")

leaflet(options = leafletOptions(minZoom = 2 )) %>% 
  addTiles() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = wrld_simpl, 
              fillColor= ~pal(Riesgo),
              #fillOpacity = 0.5, 
              weight = 2, 
              color = "lightgrey",
              popup = polygon_popup2)


```

### Riesgo relativo de casos de importación por ciudad

```{r}
cities$risk <- cities$risk*100

polygon_popup1 <- paste0("Ciudad: ", cities$label, "
",
                        "Riesgo relativo: ", round(cities$risk,4), "%")

leaflet() %>% 
  addTiles(options = leafletOptions(minZoom = 2 )) %>% 
  addProviderTiles("CartoDB.Positron") %>%
  #fitBounds(-127.44,24.05,-65.30,50.35) %>% 
  addCircleMarkers(cities$lng, 
                   cities$lat, 
                   radius = cities$risk, 
                   fill = T,
                   fillOpacity = 0.2,
                   opacity = 0.6,
                   popup = polygon_popup1)
```